Scikit-Learn is a powerful Python library for machine learning. It provides simple and efficient tools for data analysis and modeling. This tutorial will guide you through the basics of Scikit-Learn, including installing the library, loading data, and building models.
Install Scikit-Learn
To install Scikit-Learn, you can use pip:
pip install scikit-learn
Load Data
Scikit-Learn provides various datasets for you to practice with. One popular dataset is the Iris dataset, which contains measurements of three different types of iris flowers.
from sklearn.datasets import load_iris
iris = load_iris()
Build a Model
Now that we have the data loaded, let's build a model. We'll use a simple decision tree classifier:
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(iris.data, iris.target)
Evaluate the Model
After training the model, we need to evaluate its performance. We can use the accuracy score:
from sklearn.metrics import accuracy_score
predictions = clf.predict(iris.data)
accuracy = accuracy_score(iris.target, predictions)
print(f"Accuracy: {accuracy}")
Further Reading
For more information on Scikit-Learn, check out the official documentation: Scikit-Learn Documentation